A Neural Network Based Cognitive Engine for IEEE 802.11 WLAN Access Point Selection

Now-a-days IEEE 802.11 WLANs are widely deployed; in spite of this, the issue of designing an efficient and practical access point selection schemes that can provide the best throughput performance in a variety of link conditions is still open. In this paper, the authors present a cognitive AP selection scheme that allows the mobile station to learn from its past experience how to select the best AP. In their proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and a cognitive engine based on a neural network trained on this data drives the AP selection process.